Compositional Temporal Analysis Method for Fixed Priority Pre-emptive Scheduled Modal Stream Processing Applications

2016 ◽  
Author(s):  
Guus Kuiper ◽  
Stefan J. Geuns ◽  
Joost P.H.M. Hausmans ◽  
Marco J.G. Bekooij
Keyword(s):  
Stream Processing ◽  
Temporal Analysis ◽  
Analysis Method ◽  
Fixed Priority

scholarly journals MULTI-DIMENSIONAL ERROR ANALYSIS OF NEARSHORE WAVE MODELING TOOLS, WITH APPLICATION TOWARD DATA-DRIVEN BOUNDARY CORRECTION

2011 ◽  
Vol 1 (32) ◽  
pp. 67
Author(s):  
Boyang Jiang ◽  
James Kaihatu
Keyword(s):  
Boundary Conditions ◽  
Initial Boundary ◽  
Temporal Analysis ◽  
Analysis Method ◽  
Modeling Tools ◽  
Dimensional Error ◽  
Lateral Boundary Conditions ◽  
Boundary Information ◽  
The Subject ◽  
Spatio Temporal

As the forecasting models become more sophisticated in their physics and possible depictions of the nearshore hydrodynamics, they also become increasingly sensitive to errors in the inputs, such as errors in the specification of boundary information (lateral boundary conditions, initial boundary conditions, etc). Evaluation of the errors on the boundary is less straightforward, and is the subject of this study. The model under investigation herein is the Delft3D modeling suite, developed at Deltares (formerly Delft Hydraulics) in Delft, the Netherlands. Coupling of the wave (SWAN) and hydrodynamic (FLOW) model requires care at the lateral boundaries in order to balance run time and error growth. To this extent, we will use perturbation method and spatio-temporal analysis method such as Empirical Orthogonal Function (EOF) analysis to determine the various scales of motion in the flow field and the extent of their response to imposed boundary errors.

Global Fixed Priority Scheduling with Constructing Execution Dependency in Multiprocessor Real-Time Systems

2018 ◽  
Vol 27 (10) ◽  
pp. 1850165
Author(s):  
Meiling Han ◽  
Tianyu Zhang ◽  
Yuhan Lin ◽  
Zhiwei Feng ◽  
Qingxu Deng
Keyword(s):  
Real Time ◽  
Real Time Systems ◽  
Analysis Method ◽  
Priority Scheduling ◽  
Fixed Priority ◽  
Fixed Priority Scheduling ◽  
Lower Priority ◽  
Acceptance Ratio ◽  
Processor Performance ◽  
Time Systems

The increasing demands for processor performance are driving system designers to adopt multiprocessors. In this paper, we study global fixed priority scheduling in multiprocessor real-time systems and introduce a technique for improving the schedulability. The key idea is to construct execution dependency for selected tasks to leverage slack time and reduce the interference between high-priority and low-priority tasks. Thus, more lower-priority tasks are enabled to be scheduled. Further, we provide a response time analysis method which takes the execution constraint of tasks into consideration. Extensive simulation results indicate that the proposed approach outperforms existing work in terms of acceptance ratio.

scholarly journals A spatial and temporal analysis on NDVI in Urad grassland during 2010-2019 over remote sensing

2021 ◽  
Vol 336 ◽  
pp. 06029
Author(s):  
Yueying Zhang ◽  
Tiantian Liu ◽  
Yuxi Wang ◽  
Ming Zhang ◽  
Yu Zheng
Keyword(s):  
Remote Sensing ◽  
Trend Analysis ◽  
Temporal Analysis ◽  
Vegetation Coverage ◽  
Analysis Method ◽  
Modis Ndvi ◽  
The Past ◽  
Spatial And Temporal Analysis ◽  
Change Trend ◽  
Temporal And Spatial

The temporal-spatial dynamic variation of vegetation coverage from 2010 to 2019 in Urad Grassland, Inner Mongolia has been investigated by analysing on MODIS NDVI remote sensing products. This paper applies pixel dichotomy approach and linear regression trend analysis method to analyze the temporal and spatial evolution trend of vegetation coverage over the past 10 years. The average annual vegetation coverage showed a downward trend in general from 2010 to 2019. The vegetation distribution and change trend analysis provide a thorough and scientific reference for policymaking in environmental protection.

scholarly journals G-STC-M Spatio-Temporal Analysis Method for Archaeological Sites

2021 ◽  
Vol 10 (5) ◽  
pp. 312
Author(s):  
Jing Cui ◽  
Yanrong Liu ◽  
Junling Sun ◽  
Di Hu ◽  
Handong He
Keyword(s):  
Qing Dynasty ◽  
Tang Dynasty ◽  
Temporal Analysis ◽  
Space Time ◽  
Test Method ◽  
Archaeological Sites ◽  
Trend Test ◽  
Analysis Method ◽  
The Qing Dynasty ◽  
Spatio Temporal

Based on the significant hotspots analysis method (Getis-Ord Gi* significance statistics), space-time cube model (STC) and the Mann–Kendall trend test method, this paper proposes a G-STC-M spatio-temporal analysis method based on Archaeological Sites. This method can integrate spatio-temporal data variable analysis and the space-time cube model to explore the spatio-temporal distribution of Archaeological Sites. The G-STC-M method was used to conduct time slice analysis on the data of Archaeological Sites in the study area, and the spatio-temporal variation characteristics of Archaeological Sites in East China from the Tang Dynasty to the Qing Dynasty were discussed. The distribution of Archaeological Sites has temporal hotspots and spatial hotspots. Temporally, the distribution of Archaeological Sites showed a gradual increasing trend, and the number of Archaeological Sites reached the maximum in the Qing Dynasty. Spatially, the hotspots of Archaeological Sites are mainly distributed in Jiangsu (30°~33° N, 118°~121° E) and Anhui (29°~31° N, 117°~119° E) and the central region of Zhejiang (28°~31° N, 118°~121° E). Temporally and spatially, the distribution of Archaeological Sites is mainly centered in Shanghai (30°~32° N, 121°~122° E), spreading to the southern region.

scholarly journals Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis

2020 ◽  
Vol 17 (1) ◽  
pp. 360 ◽  
Author(s):  
Yu-ting Bai ◽  
Xue-bo Jin ◽  
Xiao-yi Wang ◽  
Xiao-kai Wang ◽  
Ji-ping Xu
Keyword(s):  
Correlation Analysis ◽  
Temporal Analysis ◽  
Pollution Source ◽  
Analysis Method ◽  
Dynamic Features ◽  
Dynamic Correlation ◽  
Critical Issues ◽  
Correlation Analysis Method ◽  
Spatio Temporal ◽  
Grey Relational

Pollutant analysis and pollution source tracing are critical issues in air quality management, in which correlation analysis is important for pollutant relation modeling. A dynamic correlation analysis method was proposed to meet the real-time requirement in atmospheric management. Firstly, the spatio-temporal analysis framework was designed, in which the process of data monitoring, correlation calculation, and result presentation were defined. Secondly, the core correlation calculation method was improved with an adaptive data truncation and grey relational analysis. Thirdly, based on the general framework and correlation calculation, the whole algorithm was proposed for various analysis tasks in time and space, providing the data basis for ranking and decision on pollutant effects. Finally, experiments were conducted with the practical data monitored in an industrial park of Hebei Province, China. The different pollutants in multiple monitoring stations were analyzed crosswise. The dynamic features of the results were obtained to present the variational correlation degrees from the proposed and contrast methods. The results proved that the proposed dynamic correlation analysis could quickly acquire atmospheric pollution information. Moreover, it can help to deduce the influence relation of pollutants in multiple locations.

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